A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
astro-ph.HE 3years
2026 3roles
background 1polarities
background 1representative citing papers
Energy-dependent polarization angle variability distinguishes reconnection from turbulence as the driver of blazar flares, with Mrk 421 and 1ES 1959+650 data favoring reconnection.
First results from the SPOTS campaign reveal low average optical polarization (≲10%) and low magnetic field ordering (F_B ≲0.10) across 14 TeV blazars, with stochastic or rotating polarization angles and wavelength-dependent behavior indicating complex, turbulent jet structures.
citing papers explorer
-
Advance warning of $\gamma$-ray blazar flares from \textit{Fermi}-LAT light curves: a strictly causal machine-learning backtest
A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
-
Energy-Dependent Polarization Angle Variability as a Robust Diagnostic for Blazar Flaring Mechanisms
Energy-dependent polarization angle variability distinguishes reconnection from turbulence as the driver of blazar flares, with Mrk 421 and 1ES 1959+650 data favoring reconnection.
-
Spectro-Polarimetric Observations of TeV Sources (SPOTS): First results
First results from the SPOTS campaign reveal low average optical polarization (≲10%) and low magnetic field ordering (F_B ≲0.10) across 14 TeV blazars, with stochastic or rotating polarization angles and wavelength-dependent behavior indicating complex, turbulent jet structures.